Overview

Brought to you by YData

Dataset statistics

Number of variables18
Number of observations689
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory366.1 KiB
Average record size in memory544.2 B

Variable types

Numeric9
Text2
Categorical6
DateTime1

Alerts

AnnualSalary is highly overall correlated with MonthlySalaryHigh correlation
Country is highly overall correlated with Location_IDHigh correlation
Department is highly overall correlated with Department_IDHigh correlation
Department_ID is highly overall correlated with DepartmentHigh correlation
Employee_ID is highly overall correlated with Performance_IDHigh correlation
Location_ID is highly overall correlated with CountryHigh correlation
MonthlySalary is highly overall correlated with AnnualSalaryHigh correlation
Performance_ID is highly overall correlated with Employee_IDHigh correlation
EngagementLevel is highly imbalanced (85.8%) Imbalance
Performance_ID is uniformly distributed Uniform
Employee_ID is uniformly distributed Uniform
Performance_ID has unique values Unique
Employee_ID has unique values Unique
SickLeaves has 370 (53.7%) zeros Zeros
UnpaidLeaves has 541 (78.5%) zeros Zeros
OvertimeHours has 51 (7.4%) zeros Zeros

Reproduction

Analysis started2025-03-31 02:24:48.518982
Analysis finished2025-03-31 02:25:00.590194
Duration12.07 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

Performance_ID
Real number (ℝ)

High correlation  Uniform  Unique 

Distinct689
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean345
Minimum1
Maximum689
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.5 KiB
2025-03-30T23:25:00.723812image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile35.4
Q1173
median345
Q3517
95-th percentile654.6
Maximum689
Range688
Interquartile range (IQR)344

Descriptive statistics

Standard deviation199.04145
Coefficient of variation (CV)0.57693175
Kurtosis-1.2
Mean345
Median Absolute Deviation (MAD)172
Skewness0
Sum237705
Variance39617.5
MonotonicityStrictly increasing
2025-03-30T23:25:00.890993image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.1%
464 1
 
0.1%
456 1
 
0.1%
457 1
 
0.1%
458 1
 
0.1%
459 1
 
0.1%
460 1
 
0.1%
461 1
 
0.1%
462 1
 
0.1%
463 1
 
0.1%
Other values (679) 679
98.5%
ValueCountFrequency (%)
1 1
0.1%
2 1
0.1%
3 1
0.1%
4 1
0.1%
5 1
0.1%
6 1
0.1%
7 1
0.1%
8 1
0.1%
9 1
0.1%
10 1
0.1%
ValueCountFrequency (%)
689 1
0.1%
688 1
0.1%
687 1
0.1%
686 1
0.1%
685 1
0.1%
684 1
0.1%
683 1
0.1%
682 1
0.1%
681 1
0.1%
680 1
0.1%

Employee_ID
Real number (ℝ)

High correlation  Uniform  Unique 

Distinct689
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean345
Minimum1
Maximum689
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.5 KiB
2025-03-30T23:25:01.042670image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile35.4
Q1173
median345
Q3517
95-th percentile654.6
Maximum689
Range688
Interquartile range (IQR)344

Descriptive statistics

Standard deviation199.04145
Coefficient of variation (CV)0.57693175
Kurtosis-1.2
Mean345
Median Absolute Deviation (MAD)172
Skewness0
Sum237705
Variance39617.5
MonotonicityStrictly increasing
2025-03-30T23:25:01.202068image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.1%
464 1
 
0.1%
456 1
 
0.1%
457 1
 
0.1%
458 1
 
0.1%
459 1
 
0.1%
460 1
 
0.1%
461 1
 
0.1%
462 1
 
0.1%
463 1
 
0.1%
Other values (679) 679
98.5%
ValueCountFrequency (%)
1 1
0.1%
2 1
0.1%
3 1
0.1%
4 1
0.1%
5 1
0.1%
6 1
0.1%
7 1
0.1%
8 1
0.1%
9 1
0.1%
10 1
0.1%
ValueCountFrequency (%)
689 1
0.1%
688 1
0.1%
687 1
0.1%
686 1
0.1%
685 1
0.1%
684 1
0.1%
683 1
0.1%
682 1
0.1%
681 1
0.1%
680 1
0.1%
Distinct299
Distinct (%)43.4%
Missing0
Missing (%)0.0%
Memory size42.2 KiB
2025-03-30T23:25:01.535883image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length12
Median length11
Mean length5.5268505
Min length3

Characters and Unicode

Total characters3808
Distinct characters43
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique206 ?
Unique (%)29.9%

Sample

1st rowGhadir
2nd rowOmar
3rd rowAilya
4th rowLwiy
5th rowAhmad
ValueCountFrequency (%)
muhamad 103
 
14.5%
ahmad 38
 
5.4%
abd 17
 
2.4%
alaa 13
 
1.8%
ali 12
 
1.7%
iin 10
 
1.4%
omar 10
 
1.4%
husam 9
 
1.3%
tariq 9
 
1.3%
eala 8
 
1.1%
Other values (289) 481
67.7%
2025-03-30T23:25:02.111344image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 930
24.4%
m 307
 
8.1%
h 294
 
7.7%
i 267
 
7.0%
d 263
 
6.9%
u 171
 
4.5%
A 170
 
4.5%
l 166
 
4.4%
n 162
 
4.3%
M 139
 
3.7%
Other values (33) 939
24.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3808
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 930
24.4%
m 307
 
8.1%
h 294
 
7.7%
i 267
 
7.0%
d 263
 
6.9%
u 171
 
4.5%
A 170
 
4.5%
l 166
 
4.4%
n 162
 
4.3%
M 139
 
3.7%
Other values (33) 939
24.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3808
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 930
24.4%
m 307
 
8.1%
h 294
 
7.7%
i 267
 
7.0%
d 263
 
6.9%
u 171
 
4.5%
A 170
 
4.5%
l 166
 
4.4%
n 162
 
4.3%
M 139
 
3.7%
Other values (33) 939
24.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3808
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 930
24.4%
m 307
 
8.1%
h 294
 
7.7%
i 267
 
7.0%
d 263
 
6.9%
u 171
 
4.5%
A 170
 
4.5%
l 166
 
4.4%
n 162
 
4.3%
M 139
 
3.7%
Other values (33) 939
24.7%
Distinct579
Distinct (%)84.0%
Missing0
Missing (%)0.0%
Memory size43.0 KiB
2025-03-30T23:25:02.380543image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length15
Median length12
Mean length6.6850508
Min length2

Characters and Unicode

Total characters4606
Distinct characters45
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique507 ?
Unique (%)73.6%

Sample

1st rowHmshw
2nd rowHishan
3rd rowSharaf
4th rowQbany
5th rowBikri
ValueCountFrequency (%)
abu 15
 
2.1%
almisri 9
 
1.3%
muhamad 8
 
1.1%
aldiyn 6
 
0.8%
alkhatib 5
 
0.7%
ahmad 5
 
0.7%
alhusayn 4
 
0.6%
iismaeil 3
 
0.4%
shaykh 3
 
0.4%
alrifaei 3
 
0.4%
Other values (570) 654
91.5%
2025-03-30T23:25:02.764545image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 774
16.8%
i 423
 
9.2%
l 409
 
8.9%
h 323
 
7.0%
A 309
 
6.7%
u 252
 
5.5%
r 213
 
4.6%
d 181
 
3.9%
s 180
 
3.9%
n 163
 
3.5%
Other values (35) 1379
29.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4606
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 774
16.8%
i 423
 
9.2%
l 409
 
8.9%
h 323
 
7.0%
A 309
 
6.7%
u 252
 
5.5%
r 213
 
4.6%
d 181
 
3.9%
s 180
 
3.9%
n 163
 
3.5%
Other values (35) 1379
29.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4606
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 774
16.8%
i 423
 
9.2%
l 409
 
8.9%
h 323
 
7.0%
A 309
 
6.7%
u 252
 
5.5%
r 213
 
4.6%
d 181
 
3.9%
s 180
 
3.9%
n 163
 
3.5%
Other values (35) 1379
29.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4606
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 774
16.8%
i 423
 
9.2%
l 409
 
8.9%
h 323
 
7.0%
A 309
 
6.7%
u 252
 
5.5%
r 213
 
4.6%
d 181
 
3.9%
s 180
 
3.9%
n 163
 
3.5%
Other values (35) 1379
29.9%

Gender
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size41.6 KiB
Male
449 
Female
240 

Length

Max length6
Median length4
Mean length4.6966618
Min length4

Characters and Unicode

Total characters3236
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMale
2nd rowMale
3rd rowFemale
4th rowMale
5th rowMale

Common Values

ValueCountFrequency (%)
Male 449
65.2%
Female 240
34.8%

Length

2025-03-30T23:25:02.908063image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-30T23:25:03.036065image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
male 449
65.2%
female 240
34.8%

Most occurring characters

ValueCountFrequency (%)
e 929
28.7%
a 689
21.3%
l 689
21.3%
M 449
13.9%
F 240
 
7.4%
m 240
 
7.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3236
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 929
28.7%
a 689
21.3%
l 689
21.3%
M 449
13.9%
F 240
 
7.4%
m 240
 
7.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3236
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 929
28.7%
a 689
21.3%
l 689
21.3%
M 449
13.9%
F 240
 
7.4%
m 240
 
7.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3236
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 929
28.7%
a 689
21.3%
l 689
21.3%
M 449
13.9%
F 240
 
7.4%
m 240
 
7.4%
Distinct539
Distinct (%)78.2%
Missing0
Missing (%)0.0%
Memory size5.5 KiB
Minimum2016-01-08 00:00:00
Maximum2020-12-29 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-03-30T23:25:03.163836image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-30T23:25:03.319100image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

YearsWorked
Real number (ℝ)

Distinct6
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.5776488
Minimum4
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.5 KiB
2025-03-30T23:25:03.434497image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile4
Q15
median5
Q36
95-th percentile8
Maximum9
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.2389604
Coefficient of variation (CV)0.22212952
Kurtosis-0.27363139
Mean5.5776488
Median Absolute Deviation (MAD)1
Skewness0.60784967
Sum3843
Variance1.535023
MonotonicityNot monotonic
2025-03-30T23:25:03.545802image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
5 228
33.1%
6 169
24.5%
4 142
20.6%
7 89
 
12.9%
8 51
 
7.4%
9 10
 
1.5%
ValueCountFrequency (%)
4 142
20.6%
5 228
33.1%
6 169
24.5%
7 89
 
12.9%
8 51
 
7.4%
9 10
 
1.5%
ValueCountFrequency (%)
9 10
 
1.5%
8 51
 
7.4%
7 89
 
12.9%
6 169
24.5%
5 228
33.1%
4 142
20.6%

Department_ID
Real number (ℝ)

High correlation 

Distinct20
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.085631
Minimum10
Maximum29
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.5 KiB
2025-03-30T23:25:03.657914image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile10
Q114
median19
Q324
95-th percentile28
Maximum29
Range19
Interquartile range (IQR)10

Descriptive statistics

Standard deviation5.4153029
Coefficient of variation (CV)0.28373716
Kurtosis-0.95450119
Mean19.085631
Median Absolute Deviation (MAD)5
Skewness-0.24024326
Sum13150
Variance29.325505
MonotonicityNot monotonic
2025-03-30T23:25:03.785543image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
19 140
20.3%
25 89
12.9%
10 84
12.2%
24 67
9.7%
14 58
8.4%
21 48
 
7.0%
17 40
 
5.8%
22 34
 
4.9%
28 20
 
2.9%
11 19
 
2.8%
Other values (10) 90
13.1%
ValueCountFrequency (%)
10 84
12.2%
11 19
 
2.8%
12 13
 
1.9%
13 9
 
1.3%
14 58
8.4%
15 8
 
1.2%
16 7
 
1.0%
17 40
 
5.8%
18 8
 
1.2%
19 140
20.3%
ValueCountFrequency (%)
29 16
 
2.3%
28 20
 
2.9%
27 5
 
0.7%
26 5
 
0.7%
25 89
12.9%
24 67
9.7%
23 14
 
2.0%
22 34
 
4.9%
21 48
7.0%
20 5
 
0.7%

Department
Categorical

High correlation 

Distinct20
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size48.4 KiB
Manufacturing
140 
Quality Control
89 
Account Management
84 
Quality Assurance
67 
Facilities/Engineering
58 
Other values (15)
251 

Length

Max length27
Median length20
Mean length14.780842
Min length2

Characters and Unicode

Total characters10184
Distinct characters38
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowQuality Control
2nd rowQuality Control
3rd rowMajor Mfg Projects
4th rowManufacturing
5th rowManufacturing

Common Values

ValueCountFrequency (%)
Manufacturing 140
20.3%
Quality Control 89
12.9%
Account Management 84
12.2%
Quality Assurance 67
9.7%
Facilities/Engineering 58
8.4%
Marketing 48
 
7.0%
IT 40
 
5.8%
Product Development 34
 
4.9%
Sales 20
 
2.9%
Creative 19
 
2.8%
Other values (10) 90
13.1%

Length

2025-03-30T23:25:03.912982image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
quality 156
14.8%
manufacturing 145
13.8%
control 89
 
8.4%
account 84
 
8.0%
management 84
 
8.0%
assurance 67
 
6.4%
facilities/engineering 58
 
5.5%
marketing 48
 
4.6%
it 40
 
3.8%
product 34
 
3.2%
Other values (20) 249
23.6%

Most occurring characters

ValueCountFrequency (%)
n 1145
 
11.2%
a 948
 
9.3%
t 809
 
7.9%
i 788
 
7.7%
e 767
 
7.5%
u 667
 
6.5%
r 586
 
5.8%
c 510
 
5.0%
g 439
 
4.3%
o 435
 
4.3%
Other values (28) 3090
30.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10184
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 1145
 
11.2%
a 948
 
9.3%
t 809
 
7.9%
i 788
 
7.7%
e 767
 
7.5%
u 667
 
6.5%
r 586
 
5.8%
c 510
 
5.0%
g 439
 
4.3%
o 435
 
4.3%
Other values (28) 3090
30.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10184
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 1145
 
11.2%
a 948
 
9.3%
t 809
 
7.9%
i 788
 
7.7%
e 767
 
7.5%
u 667
 
6.5%
r 586
 
5.8%
c 510
 
5.0%
g 439
 
4.3%
o 435
 
4.3%
Other values (28) 3090
30.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10184
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 1145
 
11.2%
a 948
 
9.3%
t 809
 
7.9%
i 788
 
7.7%
e 767
 
7.5%
u 667
 
6.5%
r 586
 
5.8%
c 510
 
5.0%
g 439
 
4.3%
o 435
 
4.3%
Other values (28) 3090
30.3%

Location_ID
Categorical

High correlation 

Distinct5
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size40.5 KiB
100
379 
104
156 
102
90 
103
53 
101
 
11

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2067
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row100
2nd row102
3rd row102
4th row104
5th row100

Common Values

ValueCountFrequency (%)
100 379
55.0%
104 156
22.6%
102 90
 
13.1%
103 53
 
7.7%
101 11
 
1.6%

Length

2025-03-30T23:25:04.040555image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-30T23:25:04.152553image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
100 379
55.0%
104 156
22.6%
102 90
 
13.1%
103 53
 
7.7%
101 11
 
1.6%

Most occurring characters

ValueCountFrequency (%)
0 1068
51.7%
1 700
33.9%
4 156
 
7.5%
2 90
 
4.4%
3 53
 
2.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2067
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1068
51.7%
1 700
33.9%
4 156
 
7.5%
2 90
 
4.4%
3 53
 
2.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2067
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1068
51.7%
1 700
33.9%
4 156
 
7.5%
2 90
 
4.4%
3 53
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2067
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1068
51.7%
1 700
33.9%
4 156
 
7.5%
2 90
 
4.4%
3 53
 
2.6%

Country
Categorical

High correlation 

Distinct5
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size44.8 KiB
Egypt
379 
United Arab Emirates
156 
Saudi Arabia
90 
Syria
53 
Lebanon
 
11

Length

Max length20
Median length5
Mean length9.3425254
Min length5

Characters and Unicode

Total characters6437
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEgypt
2nd rowSaudi Arabia
3rd rowSaudi Arabia
4th rowUnited Arab Emirates
5th rowEgypt

Common Values

ValueCountFrequency (%)
Egypt 379
55.0%
United Arab Emirates 156
22.6%
Saudi Arabia 90
 
13.1%
Syria 53
 
7.7%
Lebanon 11
 
1.6%

Length

2025-03-30T23:25:04.279867image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-30T23:25:04.410674image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
egypt 379
34.7%
united 156
14.3%
arab 156
14.3%
emirates 156
14.3%
saudi 90
 
8.2%
arabia 90
 
8.2%
syria 53
 
4.9%
lebanon 11
 
1.0%

Most occurring characters

ValueCountFrequency (%)
t 691
10.7%
a 646
 
10.0%
i 545
 
8.5%
E 535
 
8.3%
r 455
 
7.1%
y 432
 
6.7%
402
 
6.2%
g 379
 
5.9%
p 379
 
5.9%
e 323
 
5.0%
Other values (11) 1650
25.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6437
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 691
10.7%
a 646
 
10.0%
i 545
 
8.5%
E 535
 
8.3%
r 455
 
7.1%
y 432
 
6.7%
402
 
6.2%
g 379
 
5.9%
p 379
 
5.9%
e 323
 
5.0%
Other values (11) 1650
25.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6437
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 691
10.7%
a 646
 
10.0%
i 545
 
8.5%
E 535
 
8.3%
r 455
 
7.1%
y 432
 
6.7%
402
 
6.2%
g 379
 
5.9%
p 379
 
5.9%
e 323
 
5.0%
Other values (11) 1650
25.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6437
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 691
10.7%
a 646
 
10.0%
i 545
 
8.5%
E 535
 
8.3%
r 455
 
7.1%
y 432
 
6.7%
402
 
6.2%
g 379
 
5.9%
p 379
 
5.9%
e 323
 
5.0%
Other values (11) 1650
25.6%

MonthlySalary
Real number (ℝ)

High correlation 

Distinct613
Distinct (%)89.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2068.2017
Minimum703
Maximum3450
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.5 KiB
2025-03-30T23:25:04.542038image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum703
5-th percentile885.8
Q11436
median2077
Q32682
95-th percentile3279.2
Maximum3450
Range2747
Interquartile range (IQR)1246

Descriptive statistics

Standard deviation763.28924
Coefficient of variation (CV)0.36905937
Kurtosis-1.1111124
Mean2068.2017
Median Absolute Deviation (MAD)633
Skewness0.0032375832
Sum1424991
Variance582610.46
MonotonicityNot monotonic
2025-03-30T23:25:04.681786image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2009 3
 
0.4%
2180 3
 
0.4%
3138 3
 
0.4%
2542 2
 
0.3%
1406 2
 
0.3%
1036 2
 
0.3%
1867 2
 
0.3%
899 2
 
0.3%
3017 2
 
0.3%
2527 2
 
0.3%
Other values (603) 666
96.7%
ValueCountFrequency (%)
703 1
0.1%
705 1
0.1%
707 1
0.1%
711 1
0.1%
716 1
0.1%
719 1
0.1%
723 1
0.1%
734 1
0.1%
741 1
0.1%
744 1
0.1%
ValueCountFrequency (%)
3450 1
0.1%
3446 1
0.1%
3443 1
0.1%
3428 1
0.1%
3420 1
0.1%
3418 1
0.1%
3414 1
0.1%
3412 1
0.1%
3411 1
0.1%
3410 1
0.1%

AnnualSalary
Real number (ℝ)

High correlation 

Distinct613
Distinct (%)89.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24818.421
Minimum8436
Maximum41400
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.5 KiB
2025-03-30T23:25:04.820960image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum8436
5-th percentile10629.6
Q117232
median24924
Q332184
95-th percentile39350.4
Maximum41400
Range32964
Interquartile range (IQR)14952

Descriptive statistics

Standard deviation9159.4709
Coefficient of variation (CV)0.36905937
Kurtosis-1.1111124
Mean24818.421
Median Absolute Deviation (MAD)7596
Skewness0.0032375832
Sum17099892
Variance83895907
MonotonicityNot monotonic
2025-03-30T23:25:04.964829image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24108 3
 
0.4%
26160 3
 
0.4%
37656 3
 
0.4%
30504 2
 
0.3%
16872 2
 
0.3%
12432 2
 
0.3%
22404 2
 
0.3%
10788 2
 
0.3%
36204 2
 
0.3%
30324 2
 
0.3%
Other values (603) 666
96.7%
ValueCountFrequency (%)
8436 1
0.1%
8460 1
0.1%
8484 1
0.1%
8532 1
0.1%
8592 1
0.1%
8628 1
0.1%
8676 1
0.1%
8808 1
0.1%
8892 1
0.1%
8928 1
0.1%
ValueCountFrequency (%)
41400 1
0.1%
41352 1
0.1%
41316 1
0.1%
41136 1
0.1%
41040 1
0.1%
41016 1
0.1%
40968 1
0.1%
40944 1
0.1%
40932 1
0.1%
40920 1
0.1%

JobRate
Categorical

Distinct5
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size40.5 KiB
5.0
215 
3.0
208 
4.5
124 
2.0
72 
1.0
70 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2067
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row1.0
3rd row2.0
4th row3.0
5th row5.0

Common Values

ValueCountFrequency (%)
5.0 215
31.2%
3.0 208
30.2%
4.5 124
18.0%
2.0 72
 
10.4%
1.0 70
 
10.2%

Length

2025-03-30T23:25:05.092112image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-30T23:25:05.203150image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
5.0 215
31.2%
3.0 208
30.2%
4.5 124
18.0%
2.0 72
 
10.4%
1.0 70
 
10.2%

Most occurring characters

ValueCountFrequency (%)
. 689
33.3%
0 565
27.3%
5 339
16.4%
3 208
 
10.1%
4 124
 
6.0%
2 72
 
3.5%
1 70
 
3.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2067
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 689
33.3%
0 565
27.3%
5 339
16.4%
3 208
 
10.1%
4 124
 
6.0%
2 72
 
3.5%
1 70
 
3.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2067
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 689
33.3%
0 565
27.3%
5 339
16.4%
3 208
 
10.1%
4 124
 
6.0%
2 72
 
3.5%
1 70
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2067
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 689
33.3%
0 565
27.3%
5 339
16.4%
3 208
 
10.1%
4 124
 
6.0%
2 72
 
3.5%
1 70
 
3.4%

SickLeaves
Real number (ℝ)

Zeros 

Distinct7
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.6095791
Minimum0
Maximum6
Zeros370
Zeros (%)53.7%
Negative0
Negative (%)0.0%
Memory size5.5 KiB
2025-03-30T23:25:05.315142image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q33
95-th percentile6
Maximum6
Range6
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.1960512
Coefficient of variation (CV)1.3643636
Kurtosis-0.51717692
Mean1.6095791
Median Absolute Deviation (MAD)0
Skewness1.0378415
Sum1109
Variance4.8226407
MonotonicityNot monotonic
2025-03-30T23:25:05.427464image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 370
53.7%
1 87
 
12.6%
6 82
 
11.9%
5 40
 
5.8%
2 37
 
5.4%
4 37
 
5.4%
3 36
 
5.2%
ValueCountFrequency (%)
0 370
53.7%
1 87
 
12.6%
2 37
 
5.4%
3 36
 
5.2%
4 37
 
5.4%
5 40
 
5.8%
6 82
 
11.9%
ValueCountFrequency (%)
6 82
 
11.9%
5 40
 
5.8%
4 37
 
5.4%
3 36
 
5.2%
2 37
 
5.4%
1 87
 
12.6%
0 370
53.7%

UnpaidLeaves
Real number (ℝ)

Zeros 

Distinct7
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.75907112
Minimum0
Maximum6
Zeros541
Zeros (%)78.5%
Negative0
Negative (%)0.0%
Memory size5.5 KiB
2025-03-30T23:25:05.541808image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile5
Maximum6
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.6477636
Coefficient of variation (CV)2.1707631
Kurtosis2.8176389
Mean0.75907112
Median Absolute Deviation (MAD)0
Skewness2.0546125
Sum523
Variance2.7151247
MonotonicityNot monotonic
2025-03-30T23:25:05.635420image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 541
78.5%
4 30
 
4.4%
5 29
 
4.2%
1 26
 
3.8%
3 22
 
3.2%
6 21
 
3.0%
2 20
 
2.9%
ValueCountFrequency (%)
0 541
78.5%
1 26
 
3.8%
2 20
 
2.9%
3 22
 
3.2%
4 30
 
4.4%
5 29
 
4.2%
6 21
 
3.0%
ValueCountFrequency (%)
6 21
 
3.0%
5 29
 
4.2%
4 30
 
4.4%
3 22
 
3.2%
2 20
 
2.9%
1 26
 
3.8%
0 541
78.5%

OvertimeHours
Real number (ℝ)

Zeros 

Distinct74
Distinct (%)10.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.702467
Minimum0
Maximum198
Zeros51
Zeros (%)7.4%
Negative0
Negative (%)0.0%
Memory size5.5 KiB
2025-03-30T23:25:05.778729image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median7
Q310
95-th percentile77.6
Maximum198
Range198
Interquartile range (IQR)7

Descriptive statistics

Standard deviation25.692049
Coefficient of variation (CV)1.8749944
Kurtosis15.778779
Mean13.702467
Median Absolute Deviation (MAD)3
Skewness3.7133346
Sum9441
Variance660.0814
MonotonicityNot monotonic
2025-03-30T23:25:05.936311image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10 63
 
9.1%
7 61
 
8.9%
8 59
 
8.6%
0 51
 
7.4%
2 49
 
7.1%
3 49
 
7.1%
9 48
 
7.0%
6 47
 
6.8%
1 47
 
6.8%
4 45
 
6.5%
Other values (64) 170
24.7%
ValueCountFrequency (%)
0 51
7.4%
1 47
6.8%
2 49
7.1%
3 49
7.1%
4 45
6.5%
5 42
6.1%
6 47
6.8%
7 61
8.9%
8 59
8.6%
9 48
7.0%
ValueCountFrequency (%)
198 1
0.1%
192 1
0.1%
183 1
0.1%
153 2
0.3%
148 1
0.1%
121 1
0.1%
116 1
0.1%
111 1
0.1%
109 1
0.1%
105 1
0.1%

EngagementLevel
Categorical

Imbalance 

Distinct3
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size42.5 KiB
Medium
666 
High
 
21
Low
 
2

Length

Max length6
Median length6
Mean length5.9303338
Min length3

Characters and Unicode

Total characters4086
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMedium
2nd rowMedium
3rd rowMedium
4th rowMedium
5th rowMedium

Common Values

ValueCountFrequency (%)
Medium 666
96.7%
High 21
 
3.0%
Low 2
 
0.3%

Length

2025-03-30T23:25:06.080743image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-30T23:25:06.176972image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
medium 666
96.7%
high 21
 
3.0%
low 2
 
0.3%

Most occurring characters

ValueCountFrequency (%)
i 687
16.8%
M 666
16.3%
e 666
16.3%
d 666
16.3%
u 666
16.3%
m 666
16.3%
H 21
 
0.5%
g 21
 
0.5%
h 21
 
0.5%
L 2
 
< 0.1%
Other values (2) 4
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4086
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 687
16.8%
M 666
16.3%
e 666
16.3%
d 666
16.3%
u 666
16.3%
m 666
16.3%
H 21
 
0.5%
g 21
 
0.5%
h 21
 
0.5%
L 2
 
< 0.1%
Other values (2) 4
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4086
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 687
16.8%
M 666
16.3%
e 666
16.3%
d 666
16.3%
u 666
16.3%
m 666
16.3%
H 21
 
0.5%
g 21
 
0.5%
h 21
 
0.5%
L 2
 
< 0.1%
Other values (2) 4
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4086
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 687
16.8%
M 666
16.3%
e 666
16.3%
d 666
16.3%
u 666
16.3%
m 666
16.3%
H 21
 
0.5%
g 21
 
0.5%
h 21
 
0.5%
L 2
 
< 0.1%
Other values (2) 4
 
0.1%

Interactions

2025-03-30T23:24:59.013075image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-30T23:24:49.559026image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
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2025-03-30T23:24:50.129617image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
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2025-03-30T23:24:52.437382image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
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2025-03-30T23:24:58.432987image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-30T23:24:59.630258image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-30T23:24:50.262939image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-30T23:24:51.463171image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-30T23:24:52.549801image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-30T23:24:53.662733image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-30T23:24:54.842332image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-30T23:24:56.233874image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-30T23:24:57.445424image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-30T23:24:58.545690image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-30T23:24:59.745156image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-30T23:24:50.396343image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-30T23:24:51.589397image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-30T23:24:52.662860image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-30T23:24:53.789484image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-30T23:24:55.000163image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-30T23:24:56.473230image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-30T23:24:57.545065image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-30T23:24:58.657297image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-30T23:24:59.872350image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-30T23:24:50.591748image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-30T23:24:51.710321image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-30T23:24:52.782579image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-30T23:24:53.933220image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-30T23:24:55.166127image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-30T23:24:56.589240image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-30T23:24:57.668314image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-30T23:24:58.774831image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-30T23:24:59.994250image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-30T23:24:50.696506image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-30T23:24:51.834593image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-30T23:24:52.901232image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-30T23:24:54.106007image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-30T23:24:55.330023image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-30T23:24:56.715559image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-30T23:24:57.782355image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-30T23:24:58.888425image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Correlations

2025-03-30T23:25:06.277602image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
AnnualSalaryCountryDepartmentDepartment_IDEmployee_IDEngagementLevelGenderJobRateLocation_IDMonthlySalaryOvertimeHoursPerformance_IDSickLeavesUnpaidLeavesYearsWorked
AnnualSalary1.0000.0000.039-0.0060.0150.0000.0570.0000.0001.000-0.0980.015-0.0200.020-0.051
Country0.0001.0000.0710.0000.0520.0690.0480.0001.0000.0000.0730.0520.0480.0000.052
Department0.0390.0711.0000.9930.0000.2200.0000.0260.0710.0390.0490.0000.0000.0000.067
Department_ID-0.0060.0000.9931.000-0.0860.1530.0000.0290.000-0.006-0.004-0.086-0.0440.042-0.007
Employee_ID0.0150.0520.000-0.0861.0000.0970.0000.0210.0520.015-0.1161.000-0.027-0.009-0.012
EngagementLevel0.0000.0690.2200.1530.0971.0000.0000.1390.0690.0000.4440.0970.1020.1220.000
Gender0.0570.0480.0000.0000.0000.0001.0000.0000.0480.0570.0970.0000.0860.0850.000
JobRate0.0000.0000.0260.0290.0210.1390.0001.0000.0000.0000.0000.0210.0590.0000.055
Location_ID0.0001.0000.0710.0000.0520.0690.0480.0001.0000.0000.0730.0520.0480.0000.052
MonthlySalary1.0000.0000.039-0.0060.0150.0000.0570.0000.0001.000-0.0980.015-0.0200.020-0.051
OvertimeHours-0.0980.0730.049-0.004-0.1160.4440.0970.0000.073-0.0981.000-0.116-0.002-0.025-0.052
Performance_ID0.0150.0520.000-0.0861.0000.0970.0000.0210.0520.015-0.1161.000-0.027-0.009-0.012
SickLeaves-0.0200.0480.000-0.044-0.0270.1020.0860.0590.048-0.020-0.002-0.0271.0000.012-0.027
UnpaidLeaves0.0200.0000.0000.042-0.0090.1220.0850.0000.0000.020-0.025-0.0090.0121.000-0.011
YearsWorked-0.0510.0520.067-0.007-0.0120.0000.0000.0550.052-0.051-0.052-0.012-0.027-0.0111.000

Missing values

2025-03-30T23:25:00.191043image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
A simple visualization of nullity by column.
2025-03-30T23:25:00.460718image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Performance_IDEmployee_IDFirstNameLastNameGenderStartDateYearsWorkedDepartment_IDDepartmentLocation_IDCountryMonthlySalaryAnnualSalaryJobRateSickLeavesUnpaidLeavesOvertimeHoursEngagementLevel
011GhadirHmshwMale2018-04-04625Quality Control100Egypt1560187203.010183Medium
122OmarHishanMale2020-05-21425Quality Control102Saudi Arabia3247389641.005198Medium
233AilyaSharafFemale2017-09-28718Major Mfg Projects102Saudi Arabia2506300722.003192Medium
344LwiyQbanyMale2018-08-14619Manufacturing104United Arab Emirates1828219363.0007Medium
455AhmadBikriMale2020-03-11419Manufacturing100Egypt970116405.005121Medium
566MuhamadZueitrMale2016-02-02922Product Development102Saudi Arabia2332279843.0308Medium
677IinAlhalaliuFemale2020-05-08428Sales104United Arab Emirates1959235083.060116Medium
788MuhamadAlayaMale2018-02-10710Account Management100Egypt3394407285.0007Medium
899SusinAlmilatFemale2018-03-11615Green Building100Egypt1479177484.500105High
91010MuhamadAlrifaeiMale2020-01-03510Account Management100Egypt1186142324.510153High
Performance_IDEmployee_IDFirstNameLastNameGenderStartDateYearsWorkedDepartment_IDDepartmentLocation_IDCountryMonthlySalaryAnnualSalaryJobRateSickLeavesUnpaidLeavesOvertimeHoursEngagementLevel
679680680AlysiaAlyaghshiuFemale2019-10-19529Training104United Arab Emirates3334400081.0004Medium
680681681AyhamAlqadiMale2020-05-30419Manufacturing103Syria2574308883.0304Medium
681682682DieaShuelanMale2019-03-06519Manufacturing100Egypt1123134761.0007Medium
682683683HusamAlsaedMale2016-05-12810Account Management104United Arab Emirates2147257643.0042Medium
683684684AsdBwfaeurMale2017-06-27717IT100Egypt2929351484.5002Medium
684685685SariHannaMale2020-05-26421Marketing101Lebanon1452174242.0031Medium
685686686EubaydaKaydMale2020-06-03414Facilities/Engineering100Egypt3237388443.0104Medium
686687687KhalilAlkaluMale2017-07-11714Facilities/Engineering100Egypt2819338285.0000Medium
687688688MuhamadShrbjyMale2018-05-30611Creative100Egypt2069248283.00010Medium
688689689Abd AlbasitAlAhmarMale2020-08-05417IT104United Arab Emirates2606312725.0000Medium